Playing media items using network connected computing devices has gained popularity in today's society. Internet-based music service providers, such as Last.fm Ltd. and Spotify Ltd., have popularized the practice of providing real-time playback status information of users to a central server, central system, or other computing device. The service may then analyze the information and use it for recommending music to other users.
Currently-available music recommendation systems have made use of co-occurrence information within media sets to develop high-quality recommendation systems. Example systems include Apple Inc.'s ITUNES® GENIUS® service, and before that Strands, Inc.'s media recommender service. Using the ITUNES® GENIUS® service as an example, a central system collects information about the media collections of multiple users, and the service has access to a large and rich data set. The system then proceeds to analyze each user collection, and counts each time two media items occur within each collection. When analyzed over a large number of collections, information is produced that indicates the likelihood of a second media item being present, given the presence of a first media item. This relationship may also be referred to as the affinity between the two items. An enhancement to this algorithm includes assigning a higher bond between two media items occurring within a user created playlist within a collection, versus just occurring within the same collection. Basically, if two items occur within the same collection, it may be reasoned that they are related in some way (i.e., the same user likes both media items enough to own them). Likewise, if a user went through the effort of creating a custom playlist containing two media items, then they must be highly related.
It is desired to provide improved techniques and systems for generating music recommendations.